Mesh-to-Video Generation

• Mesh-to-video generation is a framework that converts 3D mesh assets into photorealistic, temporally consistent video sequences using diffusion models and geometric priors.
• It integrates explicit mesh animation, UV-synchronized texture synthesis, and hybrid rendering to achieve high fidelity and controllable content.
• The approach tackles challenges like temporal coherence and occlusion handling, as demonstrated in frameworks such as CT4D and Tex4D.

Mesh-to-video generation denotes a family of computational frameworks that synthesize temporally coherent video sequences or 4D content from geometric mesh inputs. This paradigm unifies advances in geometric signal processing, diffusion modeling, and animation, enabling precise scene or character control while leveraging powerful data priors. Mesh-to-video methods bridge the explicit control of 3D mesh representations (including topology, geometry, and often UV texture) with the image- and video-level fidelity afforded by deep generative models, particularly diffusion models. Research to date establishes several architectural families—explicit mesh-based animation pipelines, mesh-conditioned video diffusion, UV-synchronized texture synthesis, and mesh-oriented hybrid renderers—with various degrees of mesh representation, temporal coherence, and editability.

1. Principles of Mesh-to-Video Generation

Mesh-to-video generation fundamentally converts 3D mesh assets—either static or time-varying—into photorealistic, temporally consistent video sequences. The input mesh may encode shape, articulation, and, in many approaches, UV parameterization for appearance. The methodologies universally address two joint objectives: (a) geometric consistency (spatial alignment with the mesh structure throughout the video) and (b) temporal coherence (frame-to-frame consistency in appearance and motion). Controlled mesh-driven animation (rigging, skinning, handle manipulation), mesh-derived geometric priors (depth, normal, UV correspondences), and diffusion models guide the visual realization per-frame.

State-of-the-art approaches such as CT4D (Chen et al., 15 Aug 2024), EX-4D (Hu et al., 5 Jun 2025), VideoFrom3D (Kim et al., 22 Sep 2025), DaS (Gu et al., 7 Jan 2025), MeSS (Chen et al., 21 Aug 2025), Tex4D (Bao et al., 14 Oct 2024), and Generative Rendering (Cai et al., 2023) operationalize these principles in multistage or hybrid pipelines.

2. Core Methodologies and Architectural Variants

Mesh-to-video pipelines vary in their signal pathways, integration of explicit geometry, and handling of temporal consistency:

• Explicit mesh animation pipelines (e.g., CT4D) sequentially generate, refine, and animate meshes purely via geometric manipulations. CT4D first synthesizes a static mesh aligned to the input text prompt using a NeRF radiance field and multiview diffusion priors, refines geometry and texture by incorporating multiview, depth-normal, and single-view diffusion guidance, and finally animates the mesh with region-level, handle-driven deformations and as-rigid-as-possible (ARAP) regularization, ensuring global surface coherence (Chen et al., 15 Aug 2024).
• Video diffusion–guided mesh animation (e.g., Animating the Uncaptured) first renders a mesh to an anchor frame and uses a strong text-to-video diffusion model to hallucinate full animated sequences, which are then tracked to extract mesh motion via SMPL parameter optimization (Millán et al., 20 Mar 2025). This approach leverages motion-rich video priors while maintaining geometric control.
• Mesh-conditioned video diffusion (e.g., DaS, EX-4D, VideoFrom3D) encodes a mesh’s temporal dynamics (e.g., as a colored tracking video or via watertight mesh and occlusion priors) into latent control signals supplied to a video diffusion model (Gu et al., 7 Jan 2025, Hu et al., 5 Jun 2025, Kim et al., 22 Sep 2025). These frameworks may employ LoRA-adapted video denoisers, multi-branch ControlNet–style architecture, or sparse anchor-view propagation to interpolate, inpaint, or directly denoise mesh-anchored video frames.
• Texture-synchronized synthesis (e.g., Tex4D, Generative Rendering) achieves multi-view and temporal consistency by aggregating denoised latents in the mesh UV domain. Views are rendered, transformed into UV space, and averaged with view-dependent weighting, after which synchronized latents are projected back for per-view refinement. Temporal linkage is further strengthened by maintaining a reference UV latent texture over the video sequence (Bao et al., 14 Oct 2024, Cai et al., 2023).
• Hybrid explicit representations (mesh + 3D Gaussian splats) as in (Cai et al., 18 Mar 2024) and (Chen et al., 21 Aug 2025), combine mesh-based surface rasterization and point-based density projection. This union exploits fine surface details from high-resolution UV texture on meshes with out-of-mesh structures (e.g., hair, fine geometry) modeled via 3D splatting, composited in real time for video synthesis.

3. Mathematical Formulations and Optimization

A key element across mesh-to-video frameworks is integration of geometric priors and diffusion objectives. This is instantiated via:

• Score Distillation Sampling (SDS):

$$L_{\mathrm{SDS}} = \mathbb{E}_{t,\epsilon}\left[ w(t)\cdot\|\epsilon - \epsilon_\theta(x_t, c)\|^2 \right]$$

where $x_t$ is the (possibly multiview) render of the mesh, $c$ the conditioning input (e.g., text, depth), and $w(t)$ the time-dependent weighting (Chen et al., 15 Aug 2024).

• UV-space latent aggregation and synchrony (Tex4D):

$$Z_t(u,v)=\frac{\sum_{v=1}^V \mathcal{R}^{-1}\left(z_t^v, c_v\right)\cdot\left[\cos(\theta^v)\right]^\alpha}{\sum_{v=1}^V \left[\cos(\theta^v)\right]^\alpha}$$

Centroid aggregation brings per-view DDIM denoising into a unified UV parameter space, and the variance-corrected DDIM update in UV addresses aggregation-induced blurring artifacts (Bao et al., 14 Oct 2024).

• Handle-driven deformation and mesh partitioning (CT4D): The mesh is partitioned into $N_k$ regions by $k$-means; each is driven by rigid translation/rotation. Each mesh vertex $v_i$ is updated at frame $n$ via:

$$v_{n,i} = \sum_{k=1}^{N_k} s_{i,k} \left[ R_{n,k} (v_i-h_k) + t_{n,k} + h_k \right]$$

with periodic ARAP rigidity regularization:

$$L_{\text{rig}} = \lambda_1 \sum_{n=1}^N E_{\text{ARAP}}(\varphi_0, \varphi_n) + \lambda_2 \sum_{n=1}^N \|V'_{\text{fps}, n} - V_{\text{fps}, n}\|^2$$

• Video diffusion denoising and LoRA adaptation (EX-4D, DaS): The standard DDPM or DDIM objective is augmented with mesh-derived latent priors (e.g., DW-Mesh, tracking video), injected at each U-Net or transformer layer. LoRA adapters modulate self-attention and feedforward projections, with loss:

$$\mathcal{L}_{\rm diff} = \mathbb{E}_{t,\epsilon}\left[\omega(t)\|\epsilon_\theta(z_t, I_1, V_T, V_O, t) - \epsilon\|_2^2\right]$$

(Hu et al., 5 Jun 2025, Gu et al., 7 Jan 2025).

4. Pipeline Summaries and System Comparisons

Framework	Geometric Prior	Diffusion Stage(s)	Main Technical Strategies
CT4D (Chen et al., 15 Aug 2024)	Animatable mesh	2D/3D+video SDS	GRA algorithm; region partition
Tex4D (Bao et al., 14 Oct 2024)	UV-param mesh seq	Video (CTRL-Adapter)	UV-latent aggregation, ref. UV
EX-4D (Hu et al., 5 Jun 2025)	DW-Mesh (per-frame)	Video (Wan2.1+LoRA)	Mask simulation, depth prior
VideoFrom3D (Kim et al., 22 Sep 2025)	Coarse mesh + flows	Image+video diffusion	Anchor gen, inbetweening
DaS (Gu et al., 7 Jan 2025)	3D tracking video	Video (DiT)	Condition DiT, mesh-to-video
MeSS (Chen et al., 21 Aug 2025)	City mesh + semantics	Image+video (LCM)	Outpainting, AGInpaint, GCAlign
Hybrid Avatar (Cai et al., 18 Mar 2024)	UV-mesh+3DGS	No diffusion (explicit)	Joint opt, blended z-buffers

Notably, explicit mesh-animation (CT4D) and UV-synchronized texturing (Tex4D) achieve strong editability and multi-object compositionality. Methods leveraging hybrid explicit representations (Hybrid Avatar, MeSS) optimize for real-time or large-scale scene synthesis and can support continuous rendering or downstream relighting.

5. Evaluation Metrics and Experimental Outcomes

Evaluation protocols uniformly emphasize geometric preservation and cross-frame coherence, utilizing:

• CLIP scores on RGB, depth, and normal sequences for semantic/text alignment and structural quality (Chen et al., 15 Aug 2024).
• Temporal consistency via interframe cosine similarity (CLIP, DINO) (Bao et al., 14 Oct 2024, Chen et al., 15 Aug 2024).
• Perceptual scores and user studies (e.g., preference rates on physical consistency, VBench metrics) (Hu et al., 5 Jun 2025).
• Fidelity metrics (FID, FVD, LPIPS, PSNR, SSIM), both globally and on masked/background regions (Kim et al., 22 Sep 2025, Chen et al., 21 Aug 2025).
• Explicit geometry-aware measurements such as PSNR-D (depth), Subject/Background Consistency (SC/BC), and maximum acceleration (CAPE for SMPL tracking) (Millán et al., 20 Mar 2025, Kim et al., 22 Sep 2025).

CT4D reports state-of-the-art interframe consistency and geometry preservation versus NeRF and 3D Gaussian-splatting baselines, while Tex4D outperforms in Fréchet Video Distance and user study preference for spatio-temporal texture coherence (Bao et al., 14 Oct 2024, Chen et al., 15 Aug 2024). EX-4D and VideoFrom3D similarly exceed previous state-of-the-art in controlled view synthesis and structure preservation under large viewpoint shifts (Hu et al., 5 Jun 2025, Kim et al., 22 Sep 2025).

6. Editing, Extension, and Limitations

Mesh-to-video systems with explicit mesh output natively support a rich space of editing and compositional workflows. In CT4D, for example, once the geometry and animation are synthesized, further appearance editing may be performed by re-optimizing the texture stage alone under new text prompts, or multiple mesh-driven assets may be composed and animated jointly (Chen et al., 15 Aug 2024). Tex4D enables continuous reference-latent anchoring for incremental detail propagation and supports potential future extensions for dynamic lighting or higher-resolution synthesis (Bao et al., 14 Oct 2024).

Core limitations are shared across the field:

• Occlusion and depth ambiguities pose challenges for watertight mesh inference, detail preservation through self-occlusion, and handling of fine structures.
• Resolution and computational complexity: Latent-space aggregation, high-res UV maps, or frequent multi-view/ray tracing in hybrid pipelines incur significant memory and runtime costs.
• Handling of out-of-distribution dynamics: Unusual motion, pose, or sparse mesh coverage may degrade output fidelity, particularly at large deformation rates or under rare geometric/topological conditions.

Proposed remedies include multi-frame consistency losses, neural mesh refinement, integration of semantic/uncertainty priors, and adoption of newer video diffusion backbones.

7. Prospects and Open Research Directions

Future development in mesh-to-video generation is expected to center on:

• Scalable 4D mesh modeling for complex, dynamic scenes, integrating higher-level semantics and open-vocabulary text control.
• Densely interactive editing, including texture refresh under changing appearance prompts, stylization/relighting, and rigging of arbitrary topology.
• Architecture unification, combining strengths of explicit, hybrid, and implicit representations to balance editability, fidelity, and real-time rendering.
• Enhanced mesh and motion priors via multi-modal data, temporal cross-attention, and geometry-driven feature propagation.

The mesh-to-video generation landscape thus demarcates a critical bridge between parameterizable, controllable 3D scene editing and the realism and temporal coherence enabled by modern diffusion-based generative models (Chen et al., 15 Aug 2024, Bao et al., 14 Oct 2024, Millán et al., 20 Mar 2025, Hu et al., 5 Jun 2025, Chen et al., 21 Aug 2025, Kim et al., 22 Sep 2025, Gu et al., 7 Jan 2025, Cai et al., 18 Mar 2024, Cai et al., 2023).